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About This Role
Overview
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At NetApp, we have a history of helping customers turn challenges into business opportunities. That’s because we bring new thinking to age\-old problems, like how to use data most effectively in the most efficient possible way. As an Engineer with NetApp, you’ll have the opportunity to work with modern cloud and container orchestration technologies in a production setting. You’ll play an important role in scaling systems sustainably through automation and evolving them by pushing for changes to improve reliability and velocity.
Own Every Moment at NetApp
At NetApp, your ideas power innovation. We lead in intelligent data infrastructure—delivering unified storage, integrated data services, and solutions that help organizations unlock the full potential of their data, from AI to multicloud.
Ready to innovate and contribute to our path to $10B? Here, you'll collaborate with passionate teams, tackle real\-world challenges, and see your impact in how customers transform and grow. If you're ready to bring curiosity, creativity, and drive to every moment, NetApp is where your journey begins. Join teams that drive results, innovate to elevate, and excel together across every function.
Job Summary
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As an Sr Engineer on the AI Enablement team, you will be a hands\-on builder and a force multiplier for how the broader Keystone engineering org works with AI. You'll split your time between scaling engineering practice and shipping autonomous agents that do real work — not prototypes that stall after a demo.
This role requires strong software engineering fundamentals, comfort operating with ambiguity on a fast\-moving small team, and the judgment to know when an AI\-generated answer is good enough to ship versus a production risk.
Job Responsibilities
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- Scale engineering with AI
- Drive spec\-driven, agentic\-IDE development workflows (Cursor, Claude Code) across the team, from spec to implementation to review, so AI usage translates into measurable velocity, not just novelty.
- Define and evolve quality gates for AI\-generated code: what changes in review, CI, and merge criteria when a large share of PRs are AI\-assisted.
- Build and maintain shared skills, prompts, and MCP tooling that other Keystone engineers reuse, reducing redundant AI infrastructure across teams.
- Build production AI agents on the enterprise stack
- Design and ship agent workflows (planner vs. fixed\-workflow, state/retries, human\-in\-the\-loop checkpoints) that automate real toil
- Build well\-scoped MCP tools/integrations against enterprise systems with clear schemas, auth boundaries, idempotency, and explicit rules on what data never reaches the model.
- Own evals and guardrails for agents in production: offline golden sets, online sampling, canary/rollback paths, and hard checks on numeric/PII/outbound\-send correctness — distinguishing tool failures from model failures.
- Design for operational readiness from day one: what breaks under a 10× usage spike before quarter close, what the kill switch looks like when an agent gets a fact wrong, and how incidents get triaged.
Job Requirements
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- Bachelor's degree in Computer Science, Engineering, or a related field; or equivalent, relevant experience.
- 8\+ years of professional software development experience, with proven ownership of at least one system or domain end\-to\-end.
- Production experience with LLMs \- prompt engineering, agent development, and evaluation frameworks.
- Hands\-on experience building agent harnesses: tool calling, state/session management, retries, guardrails, and stop conditions for long\-running agent workflows.
- Deep, hands\-on proficiency in Python/Go/Java
- Experience with agent/orchestration frameworks (LangChain, LangGraph, or equivalent) and MCP or similar tool\-calling standards, integrated with enterprise systems
- Working knowledge of AI\-native, spec\-driven development workflows (agentic IDEs like Cursor/Claude Code) and defining quality gates for AI\-generated code.
- Strong problem decomposition and stakeholder navigation \- able to scope an ambiguous cross\-functional ask into a shippable plan and own it end\-to\-end, forward\-deployed\-engineer style.
Education
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IC \- Typically requires a minimum of 8 years of related experience.Mgr \& Exec \- Typically requires a minimum of 6 years of related experience.
Compensation:
The target salary range for this position is 170,000 \- 253,000 USD. The salary offered will be determined by the candidate's location, qualifications, experience, and education and may be outside of this range. The range is based on 'On Target Earnings’ (OTE) representing the total potential earnings, which is the sum of the base salary and potential commission earned when performance targets are achieved. Final compensation packages are competitive and in line with industry standards, reflecting a variety of factors, and include a comprehensive benefits package. This may cover Health Insurance, Life Insurance, Retirement or Pension Plans, Paid Time Off, various Leave options, employee stock purchase plan, and/or restricted stocks (RSU’s). These offerings are subject to regional variations and governed by local laws, regulations, and company policies. We will provide detailed information about the specific benefits for your region during the recruitment process.
At NetApp, we embrace a hybrid working environment designed to strengthen connection, collaboration, and culture for all employees. This means that most roles will have some level of in\-office and/or in\-person expectations, which will be shared during the recruitment process.
Equal Opportunity Employer:
NetApp is firmly committed to Equal Employment Opportunity (EEO) and to compliance with all federal, state and local laws that prohibit employment discrimination based on age, race, color, gender, sexual orientation, gender identity, national origin, religion, disability or genetic information, pregnancy, protected veteran status, and any other protected classification.
Why You'll Thrive at NetApp
At NetApp, you won't wait for the perfect moment—you'll make it. The early planning, the extra thought, the bold idea that turns good into great: That's how our people operate and how we continue to push the boundaries of data infrastructure.
NetApp is the trusted partner for organizations transforming data into opportunity. As the only enterprise\-grade storage service natively embedded in Google Cloud, AWS, and Microsoft Azure, we empower customers to run everything from traditional workloads to enterprise AI with unmatched performance, resilience, and security.
Our culture
We celebrate mold breakers, bold thinkers, and problem solvers. We reward initiative, impact, and ownership. We provide flexibility so you can balance professional ambition with your personal life. Here, differences are not just welcomed—they drive everything we do.
If you're ready to innovate, rise to the challenge, and own every moment \- make your next move your best one. now.
Submitting an Application
To ensure a streamlined and fair hiring process for all candidates, our team only reviews applications submitted through our company website. This practice allows us to track, assess, and respond to applicants efficiently. Emailing our employees, recruiters, or Human Resources personnel directly will not influence your application.
AI Disclosure
For select roles, some stages of our hiring process may use artificial intelligence tools to help evaluate applications and candidate selection. These tools support—rather than replace—human decision\-making.
Our values
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Put the customer at the center. Care for each other and our communities. Think and act like owners. Build belonging every day. Embrace a growth mindset.
Benefits
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### Volunteer time off
40 hours of paid volunteer time each year.
### Well\-being
Employee Assistance Program, fitness, and mental health resources to help employees be their best.
### Time away
Paid time off for vacation and to recharge.
Salary Context
This $170K-$253K range is above the median for AI Software Engineer roles in our dataset (median: $183K across 194 roles with salary data).
Role Details
About This Role
AI Software Engineers build the applications and systems that AI models run inside. They own the API layers, data pipelines, frontend integrations, and infrastructure that turn a model into a product users interact with. Every AI company needs engineers who can build the software around the AI.
The challenge is building reliable systems around inherently unreliable components. Models are probabilistic. They'll give different answers to the same question. They hallucinate. They're slow. They're expensive. Your job is to build an application layer that handles all of this gracefully while delivering a product that users trust and enjoy.
Across the 3,708 AI roles we're tracking, AI Software Engineer positions make up 7% of the market. At NetApp, this role fits into their broader AI and engineering organization.
AI Software Engineer roles are among the most numerous in the AI job market. Every company deploying AI needs software engineers who understand AI integration patterns. The demand is broad, spanning startups to enterprises, across every industry adopting AI capabilities.
What the Work Looks Like
A typical week includes: building API endpoints that serve model inference with caching and fallback logic, designing the data pipeline that feeds context to a RAG system, implementing streaming responses in the frontend, debugging a race condition in the async inference pipeline, and optimizing database queries for the vector search layer. It's full-stack engineering with AI at the center.
AI Software Engineer roles are among the most numerous in the AI job market. Every company deploying AI needs software engineers who understand AI integration patterns. The demand is broad, spanning startups to enterprises, across every industry adopting AI capabilities.
Skills Required
Full-stack engineering skills with AI integration experience. Python and TypeScript are the most common requirements. You'll need to understand API design, database architecture, and how to build reliable systems around probabilistic outputs. Experience with streaming, async processing, and caching patterns is increasingly important as real-time AI applications proliferate.
Knowledge of vector databases, embedding APIs, and LLM integration patterns (function calling, structured outputs, retry logic) differentiates AI software engineers from general software engineers. Understanding cost optimization (caching strategies, model routing, batched inference) is valuable since inference costs can dominate application economics.
Strong postings describe the product you'll be building, the AI integration patterns you'll work with, and the scale requirements. Look for companies that have existing AI features and need engineers to improve and expand them, not companies that are 'planning to add AI' someday.
Compensation Benchmarks
AI Software Engineer roles pay a median of $219,250 based on 424 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $200,000. Disclosed range: $170K to $253K.
Across all AI roles, the market median is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. For comparison, the highest-paying categories include AI Safety ($300,000) and Research Engineer ($280,000). By seniority level: Entry: $120,000; Mid: $200,000; Senior: $230,000; Director: $272,150; VP: $250,000.
NetApp AI Hiring
NetApp has 2 open AI roles right now. They're hiring across AI Software Engineer, AI/ML Engineer. Positions span Remote, US, San Jose, CA, US. Compensation range: $253K - $292K.
Remote Work Context
Remote AI roles pay a median of $185,334 across 717 positions. About 14% of all AI roles offer remote work.
Career Path
Common paths into AI Software Engineer roles include Software Engineer, Full-Stack Developer, Backend Engineer.
From here, career progression typically leads toward Staff Engineer, AI Architect, Engineering Manager.
If you're a software engineer, you're already 80% there. Learn the AI integration patterns: RAG, streaming inference, function calling, structured outputs. Build a project that demonstrates you can wrap an AI model in a production-quality application with proper error handling, caching, and user experience. That's the portfolio piece that gets you hired.
What to Expect in Interviews
Technical screens look like standard software engineering interviews with an AI twist. Expect system design questions about building reliable applications around probabilistic models: handling streaming responses, implementing retry logic for API failures, and designing caching strategies for LLM outputs. Coding rounds test standard algorithms plus practical integration patterns like async processing and rate limiting.
When evaluating opportunities: Strong postings describe the product you'll be building, the AI integration patterns you'll work with, and the scale requirements. Look for companies that have existing AI features and need engineers to improve and expand them, not companies that are 'planning to add AI' someday.
AI Hiring Overview
The AI job market has 3,708 open positions tracked in our dataset. By seniority: 102 entry-level, 1,705 mid-level, 1,469 senior, and 432 leadership roles (Director, VP, C-Level). Remote roles make up 14% of the market (508 positions). The remaining 3,180 roles require on-site or hybrid attendance.
The market median for AI roles is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. Highest-paying categories: AI Safety ($300,000 median, 21 roles); Research Engineer ($280,000 median, 147 roles); AI Architect ($254,798 median, 67 roles).
AI Software Engineer roles are among the most numerous in the AI job market. Every company deploying AI needs software engineers who understand AI integration patterns. The demand is broad, spanning startups to enterprises, across every industry adopting AI capabilities.
The AI Job Market Today
The AI job market spans 3,708 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (2,605), Data Scientist (310), AI Software Engineer (259). These three account for the majority of open positions, though smaller categories often have higher per-role compensation because of specialized skill requirements.
The seniority mix tells a story about where AI teams are in their maturity. Entry-level roles (102) are outnumbered by mid-level (1,705) and senior (1,469) positions, reflecting that most companies are past the 'build a team from scratch' phase and need experienced engineers who can ship production systems. Leadership roles (Director, VP, C-Level) total 432 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 14% of all AI roles (508 positions), with 3,180 requiring on-site or hybrid attendance. The remote share has stabilized after the post-pandemic correction. Senior and specialized roles (Research Scientist, ML Architect) are more likely to be remote-eligible than entry-level positions, partly because experienced hires have more negotiating power and partly because these roles require less hands-on mentorship.
AI compensation is structured in clear tiers. The market median sits at $217,500. Top-quartile roles start at $272,100, and the 90th percentile reaches $325,000. These figures include base salary with disclosed compensation. Total compensation (including equity, bonuses, and sign-on) runs 20-40% higher at companies that offer those components.
Category matters for compensation. AI Safety roles lead at $300,000 median, while Prompt Engineer roles sit at $140,000. The spread between highest and lowest-paying categories reflects the premium on specialized technical skills versus broader analytical roles.
The most in-demand skills across all AI postings: Python (1,890 postings), Aws (1,103 postings), Azure (877 postings), Rag (855 postings), Gcp (631 postings), Prompt Engineering (560 postings), Pytorch (545 postings), Claude (498 postings). Python dominates, appearing in the vast majority of role descriptions regardless of category. Cloud platform experience (AWS, GCP, Azure) is the second most common requirement. The newer entrants to the top skills list (RAG, vector databases, LLM APIs) reflect the shift from traditional ML toward generative AI applications.
Frequently Asked Questions
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